DistRS: Disaggregated Reward Service for RLVR with Batch-Level Constraint

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key post-training paradigm for enhancing the capabilities of large language models (LLMs). As the complexity increases and resource consumption grows, reward computation is becoming a critical workload in the RLVR training process. We present DistRS, a disaggregated reward service framework designed to provide resource-efficient reward computation for RLVR training. Through the analysis of a real RLVR training task, we observe that the reward service faces a highly dynamic workload, motivating the need for elasticity and multi-tenancy. DistRS leverages request-level flexibility from the request-in, batch-out characteristic of reward computation to design more resource-efficient scaling and scheduling policies. Specifically, DistRS establishes a batch-level constraint for each training task that relaxes latency requirements at the request level. Building on this foundation, we design a history-based resource scaling policy and a batch-level priority-based request scheduling policy. In addition, DistRS incorporates a timeout-aware mechanism to adjust resource allocation, thereby mitigating the impact of deviations between history and actual execution. We evaluate DistRS with real-world RLVR training tasks and the results demonstrate that DistRS reduces resource consumption by up to 3.79× while incurring minimal overhead on training progress.